Member of the Technical Staff, Interpretability
About the role
Output has built a biological reasoning model that understands biology at the scale and complexity life actually operates. Our model independently learned the principles of molecular interactions, opening up drug treatments that were previously impossible. We're already generating therapies that traditional approaches cannot reach. The hardest problems in both AI and biology are being solved here, and there is room for you to own one.
Output is currently in stealth, operated by a team of repeat founders and biotech veterans with multiple exits in AI x Bio, and backed by top-tier VCs including Y Combinator.
You will continue developing methods to understand what our foundation model learns about biology, and build the tools that make it a glass box model. We believe that in biology, a model's reasoning must be visible. And the features you find are not just explanations: they expand what the model can do.
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You will continue developing our methods for probing and reverse-engineering the model's learned representations, understanding how it encodes biological information across molecular scales
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You will design and run experiments to identify and characterize capabilities, mapping what the model has learned about molecular interactions and biological function
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You will build methods to extract the model's biological understanding as explicit, usable outputs that downstream systems and researchers can act on
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You will create tools that connect model internals to meaningful biological concepts, making the model's reasoning interpretable to scientists and useful in practice
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You will work closely with the pretraining and generation teams, feeding interpretability findings back into model development to strengthen the capabilities you uncover
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You will own the full pipeline from probing experiments to production-quality interpretability tools, building robust systems on distributed infrastructure
